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Demand side management in power grid enterprise control: A comparison of industrial & social welfare approaches

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  • Jiang, Bo
  • Muzhikyan, Aramazd
  • Farid, Amro M.
  • Youcef-Toumi, Kamal

Abstract

Despite the recognized importance of demand side management (DSM) for mitigating the impact of variable energy resources and reducing the system costs, the academic and industrial literature have taken divergent approaches to DSM implementation. The prequel to this paper has demonstrated that the netload baseline inflation – a feature particular to the industrial DSM unit commitment formulation – leads to higher and costlier day-ahead scheduling compared to the academic social welfare method. This paper now expands this analysis from a single optimization problem to the full power grid enterprise control with its multiple control layers at their associated time scales. These include unit commitment, economic dispatch and regulation services. It compares the two DSM formulations and quantifies the technical and economic impacts of industrial baseline errors in the day-ahead and real-time markets. The paper concludes that the presence of baseline errors – present only in the industrial model – leads to a cascade of additional system imbalances and costs as compared to the social welfare model. A baseline error introduced in the unit commitment problem will increase costs not just in the day-ahead market, but will also introduce a greater netload error residual in the real-time market causing additional cost and imbalances. These imbalances if left unmitigated degrade system reliability or otherwise require costly regulating reserves to achieve the same performance. An additional baseline error introduced in the economic dispatch further compounds this cascading effect with additional costs in the real-time market, amplified downstream imbalances, and further regulation capacity for its mitigation.

Suggested Citation

  • Jiang, Bo & Muzhikyan, Aramazd & Farid, Amro M. & Youcef-Toumi, Kamal, 2017. "Demand side management in power grid enterprise control: A comparison of industrial & social welfare approaches," Applied Energy, Elsevier, vol. 187(C), pages 833-846.
  • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:833-846
    DOI: 10.1016/j.apenergy.2016.10.096
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    References listed on IDEAS

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    1. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    2. Esther, B. Priya & Kumar, K. Sathish, 2016. "A survey on residential Demand Side Management architecture, approaches, optimization models and methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 342-351.
    3. Brennan, Timothy J., 1998. "Demand-Side Management Programs Under Retail Electricity Competition," Discussion Papers 10615, Resources for the Future.
    4. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    5. Walawalkar, Rahul & Fernands, Stephen & Thakur, Netra & Chevva, Konda Reddy, 2010. "Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO," Energy, Elsevier, vol. 35(4), pages 1553-1560.
    6. Walawalkar, Rahul & Blumsack, Seth & Apt, Jay & Fernands, Stephen, 2008. "An economic welfare analysis of demand response in the PJM electricity market," Energy Policy, Elsevier, vol. 36(10), pages 3692-3702, October.
    7. Yang, Hongming & Xiong, Tonglin & Qiu, Jing & Qiu, Duo & Dong, Zhao Yang, 2016. "Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response," Applied Energy, Elsevier, vol. 167(C), pages 353-365.
    8. Hung-po Chao, 2011. "Demand response in wholesale electricity markets: the choice of customer baseline," Journal of Regulatory Economics, Springer, vol. 39(1), pages 68-88, February.
    9. Jiang, Bo & Farid, Amro M. & Youcef-Toumi, Kamal, 2015. "Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches," Applied Energy, Elsevier, vol. 156(C), pages 642-654.
    10. Patteeuw, Dieter & Reynders, Glenn & Bruninx, Kenneth & Protopapadaki, Christina & Delarue, Erik & D’haeseleer, William & Saelens, Dirk & Helsen, Lieve, 2015. "CO2-abatement cost of residential heat pumps with active demand response: demand- and supply-side effects," Applied Energy, Elsevier, vol. 156(C), pages 490-501.
    11. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    12. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    13. Alimohammadisagvand, Behrang & Jokisalo, Juha & Kilpeläinen, Simo & Ali, Mubbashir & Sirén, Kai, 2016. "Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control," Applied Energy, Elsevier, vol. 174(C), pages 275-287.
    14. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    15. Menke, Ruben & Abraham, Edo & Parpas, Panos & Stoianov, Ivan, 2016. "Demonstrating demand response from water distribution system through pump scheduling," Applied Energy, Elsevier, vol. 170(C), pages 377-387.
    16. Cui, Hantao & Li, Fangxing & Hu, Qinran & Bai, Linquan & Fang, Xin, 2016. "Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants," Applied Energy, Elsevier, vol. 176(C), pages 183-195.
    17. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    18. Farid, Amro M. & Jiang, Bo & Muzhikyan, Aramazd & Youcef-Toumi, Kamal, 2016. "The need for holistic enterprise control assessment methods for the future electricity grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 669-685.
    19. Lakshmanan, Venkatachalam & Marinelli, Mattia & Hu, Junjie & Bindner, Henrik W., 2016. "Provision of secondary frequency control via demand response activation on thermostatically controlled loads: Solutions and experiences from Denmark," Applied Energy, Elsevier, vol. 173(C), pages 470-480.
    20. Wang, Ge & Zhang, Qi & Li, Hailong & McLellan, Benjamin C. & Chen, Siyuan & Li, Yan & Tian, Yulu, 2017. "Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis," Applied Energy, Elsevier, vol. 185(P2), pages 1869-1878.
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    3. Summerbell, Daniel L. & Khripko, Diana & Barlow, Claire & Hesselbach, Jens, 2017. "Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant," Applied Energy, Elsevier, vol. 197(C), pages 100-113.
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    5. Hugo Algarvio & Fernando Lopes, 2023. "Bilateral Contracting and Price-Based Demand Response in Multi-Agent Electricity Markets: A Study on Time-of-Use Tariffs," Energies, MDPI, vol. 16(2), pages 1-17, January.

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